DNA barcoding for biosecurity: case studies from the UK plant protection program
Bibliographic record
Abstract
Since its conception, DNA barcoding has seen a rapid uptake within the research community. Nevertheless, as with many new scientific tools, progression towards the point of routine deployment within diagnostic laboratories has been slow. In this paper, we discuss the application of DNA barcoding in the Defra plant health diagnostic laboratories, where DNA barcoding is used primarily for the identification of invertebrate pests. We present a series of case studies that demonstrate the successful application of DNA barcoding but also reveal some potential limitations to expanded use. The regulated plant pest, Bursephalenchus xylophilus, and one of its vectors, Monochamus alternatus, were found in dining chairs. Some traded wood products are potentially high risk, allowing the movement of longhorn beetles; Trichoferus campestris, Leptura quadrifasciata, and Trichoferus holosericeus were found in a wooden cutlery tray, a railway sleeper, and a dining chair, respectively. An outbreak of Meloidogyne fallax was identified in Allium ampeloprasum and in three weed species. Reference sequences for UK native psyllids were generated to enable the development of rapid diagnostics to be used for monitoring following the release of Aphalara itadori as a biological control agent for Fallopia japonica.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".